Thread by @simonedsaid

I’m so excited and proud to finally be able to share my most recent 2nd author paper in @CellCellPress and the cover I designed.👩🏻‍🔬👩🏻‍🎨 #sciart #science #tweeprint

Neuronal Inactivity Co-opts LTP Machinery to Drive Potassium Channel Splicing and Homeostatic Spike Widening

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Neurons can change many of their biochemical and electrical properties, AKA Plasticity. It’s hypothesized that this ability could be a substrate for learning and memory, as well as other key brain functions. Neuronal plasticity typically is categorized into two kinds:

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(1) Hebbian plasticity: “positive feedback” that let’s a neuron reinforce new stimuli. However, left unchecked Hebbian plasticity leads to instability. In comes: (2) Homeostatic plasticity: “negative feedback” that stabilizes a neuron after a long period of stimuli changes.

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Both synapses (neuronal electrochemical connections), and action potentials (electrical “spikes”) exhibit homeostatic plasticity. What our paper shows is that the homeostatic regulation of spikes paradoxically depends on synaptic hebbian plasticity! Weird!

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The series of experiments we’ve done shows that spikes will change their shape when a neuron has been inactive for a long period of time. The electrical spikes last longer. Neurons achieve this by using all kinds of cellular biology:

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(1) Synapses somehow use Hebbian synaptic plasticity to homeostatically increase synaptic strength (weird!).
(2) This signal travels from the synapse to the nucleus.
(3) When it gets to the nucleus, splicing of the mRNA of the ion channel, BK, is changed.

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(4) The newly spliced BK mRNA is translated into new BK channels that let in less current during the downswing of spikes, thus slowing down how fast a spike repolarizes (This is what my cover illustrates, using the original recorded data!)

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(5) This leads to spikes widening, thus homeostatically stabilizing a neuron after it’s encountered a long period of inactivity.

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What makes this so new and weird is that we found that homeostatic plasticity uses it’s “opposite” Hebbian plasticity to do its thing! Typically, we model these plasticities as two separate things, opposing each other to let a neuron both learn and stay stable.

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My current project is trying to solve this paradox at the synaptic level, hopefully leading neuroscience to a better understanding how neurons can do all these things. Keep an eye out for it!

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And finally, here’s the link to the full paper.
Feel free to DM me for the full text if you can’t get past the paywall.
bit.ly/2VgI8Vd

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